Three-Dimensional Initial Salt Body Building for Full-Waveform Inversion Assisted by Deep Learning

Abstract

Salt bodies represent a significant challenge when performing full-waveform inversion (FWI) due to convergence issues when retrieving high-contrast geological structures. Recent advances in FWI promote the adoption of data regularization to address this problem. However, the model complexity where the bodies are enclosed tends to be the leading cause of unsatisfactory results. In recent years, the implementation of deep learning to tackle this issue has been proposed; however, these solutions generally rely on a direct mapping between the input (gathers) and output data (2D salt bodies) that are usually builtusing large datasets to become effective. In this study, we propose a simple and robust methodology for 3D initial salt body building assisted by deep learning. Two pre-trained models identify reflections associated with salt bodies on 3D shot gathersfrom the SEG/EAGE 3D salt model. Then, a 3D initial salt body is outlinedand built with RTM using only the deep learning-identified reflections. Finally, the 3D salt body is embedded into a background velocity model to conclude the initial model building for FWI. The accuracy of the initial model is tested by performing FWI, where observedand modeled data are compared, showing satisfactory results.